Banks can open the door to a new Era of Currency Management

A growing number of central banks have access to serial number data on their banknote sorting machines, making it possible to move beyond traditional aggregated data techniques and run both big data and data analytic software. When this happens, the use of averages, with the resulting margins of error, compared with tracking actual banknotes using their serial numbers, is not necessary.

“The banknote industry is sitting at the start of a revolution in its approach to currency management,” says Lee Maisner, Managing Director of 7 Layer, a big data and software development company in the cash logistics sector. “The key to unlocking this new era is the ability to gather and analyse banknotes based on their serial number data.”

“Based on a recent 7 Layer survey, it appears around 5-10% of Central Banks currently have access to serial number data. For the remainder, we estimate another 25-30% of Central Banks have machines with serial number reading capability but cannot access this data for contractual or other reasons with their equipment supplier,” he says.

“Banks certainly have a right to see their own data and equipment manufacturers are starting to recognise this. Now that software is available to effectively analyse the data, access to serial number data is the priority,” says Mr. Maisner

With the 7 Layer survey revealing that almost 70% of central banks use vault management software, the number of data points available for an increasing number of banks to understand banknote performance is reaching a critical mass. Mr. Maisner says his company will be the catalyst for this inflection point due to the big data analytic power that the NoteChain® software solution makes available.

“NoteChain® provides data analytics using extensive computation via algorithms and programming code and the most current methods in computer science, statistics and mathematics. The program interrogates large and varied data sets to uncover hidden patterns, unseen correlations, emerging trends and previously unobserved banknote behaviours that give central banks full command over the performance of their currency management system.”

While statistical and mathematical methodology is used in data analytics, it is the algorithms within the software, the sheer volume of data analysed and the visualisatio

ns that are generated that makes data analytics such a powerful tool in generating deep and accurate insights.

“Until this point in time, the industry has been using statistical modelling which is now analysis from yesteryear. In essence, it is an old and well-established method of approximation. In reality, statistics is an approximation of reality and incomparable to applying data analytic programming capable of tracking billions of banknotes using their serial number throughout the life of each note specifically,” says Mr. Maisner.

As a basic example, suppose you want to report the fitness of different denominations in your banknote series.

The big data method involves measuring the fitness of every single banknote of each denomination that passes through banknote processing machines in a given period and connects that fitness rating to a specific serial number. NoteChain®, which does not mind what sorting machine is collecting the number, ‘ingests’ the data and has the computing power to analyse it to provide a comprehensive and exact answer to the specific questions that a Bank wants answering. It is so powerful, it will also identify trends and relationships that can only be seen through big data analytics.

In comparison, the statistical approach involves selecting a random sample of each denomination, computing the average fitness rating and standard deviation (level of confidence) and using that sample as a representation of the entire data set.

From this perspective, the gap is obvious: by reporting approximations based on representative samples, there is no detailed insight gained into the individual journey of each note and the factors that affect each note during its life cycle, nor the ability to explore the myriad relationships between all banknotes that unearths the true insights that only the algorithmic power of data analytics can provide.

“For any Central Bank with access to serial number data and the power of NoteChain®, the full reality of a banknote’s performance and its component parts against all variables can be understood using beautifully presented visualisations that make analysis easy,” says Mr. Maisner.

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